Big Data Environment Research Articles

Research on Big Data-Driven Rural Revitalization Sharing Cogovernance Mechanism Based on Cloud Computing Technology

The political arrangement and strategic action to realize socialist sharing in the new era is the rural revitalization strategy, which is a development strategy to complete the historical task of farmers living a better life and solve the problem of insufficient rural development. Data sharing and driving, on the other hand, remain the weak links in China’s digital village construction. This paper proposes to develop a BD (big data)-driven rural revitalization governance, sharing, and monitoring platform based on CC (cloud computing) technology to promote the reshaping of rural governance patterns and to provide systematic support for the implementation of rural revitalization strategies. Knowledge governance rules and processes for rural community public affairs are created in a BD environment. Clustering in feature space is considered to address the low efficiency caused by clustering algorithms in high-dimensional data. The method proposed in this paper is perfectly matched in both the preprocessing and clustering steps, which not only ensures the algorithm’s accuracy but also significantly improves its efficiency.

Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Aiming at the problems that the traditional model is difficult to extract information features, difficult to learn deep knowledge, and cannot automatically and effectively obtain features, which leads to the problem of low recommendation accuracy, this paper proposes a personalized tourism route recommendation model of intelligent service robot using deep learning in a big data environment. Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. Finally, the processed data is input into the trained recommendation model to generate a personalized tourism route recommendation scheme. The experimental analysis of the proposed model based on Pytorch deep learning framework shows that its Pre@10, Rec@10 values are 88% and 83%, respectively, and the mean square error is 1.537, which are better than other comparison models and closer to the real tourist route of the tourists.

Evaluation of Regional Ecological Efficiency and Intelligent Decision Support for Sustainable Development Based on Environmental Big Data.

To promote urbanization in the next stage, it is of great significance to explore the ecological efficiency of green ecological regions and advance the sustainable development of a social economic system. However, spatial heterogeneity has not been fully considered in the existing evaluation models or methods for regional ecological efficiency (REE), and the corresponding decisions on sustainable development are not the optimal solutions. To solve the problems, this paper explores the evaluation of REE and intelligent decision support for sustainable development by analyzing environmental big data. Firstly, the spatiotemporal evolution of REE was examined based on environmental big data to clarify the spatial layout of REE and the sources of the spatial differences. Next, a multiobjective optimal decision-making model was established for the sustainable development of a regional ecosystem, and the solving method was presented for the model. The proposed model was proved valid through experiments.

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era.

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

Research on opinion polarization by big data analytics capabilities in online social networks

Opinion polarization in online social networks causes a lot of concerns on its social, economic, and political impacts, and is becoming an important topic for academic research. Based on the system theory, a theoretical framework on analyzing opinion polarization combining big data analytics capabilities (BDAC) is proposed. A web crawler is used to collect data from the Sina Weibo platform on the topic of “Tangping”. Concerning the characteristics of the big data environment, social network analysis (SNA), machine learning, text clustering and content analysis are used to mine opinion polarization of “Tangping” on Weibo. Results show that social network users holding the same opinion indicate the phenomenon of aggregation. Although no influential users support the opinion of “Tangping” on Weibo, a high percentage of people advocate the idea. The supporting group has the most clusters while the opposing group has the highest density of keywords. The research contributes to the existing literature on applying BDAC to analyze online polarization from the perspective of the system from user behavior and interaction to topic clustering and keywords identification. The conceptual system framework shows superiority in the integration of information coordination of microsystem and exosystem. Guidance strategies are put forward to supplement the formation theory of opinion polarization and provide suggestions to reasonably regulate network group polarization.

Analysis of Key Factors Affecting Undergraduate Entrepreneurship Ability from a Big Data Perspective

To achieve rapid social and economic growth, society has raised expectations for the overall quality and competence of today’s college students in the environment of big data. College students must have a strong spirit of creation and entrepreneurship, as well as a precise level of professional knowledge and practical talents. Based on this backdrop, a weighted K-means clustering method (WK-means) is utilized to investigate the key determinants of college students’ entrepreneurial ability and their interactions with important text content, such as the college student innovation and entrepreneurship competition. To begin, extract a number of characteristics from the sample of entrepreneurial college students, particularly the entrepreneurial ability components. The sample is then divided into two groups. Entrepreneurs who succeed are categorized as positive, while those who fail are classified as negative. The clustering result yields the feature weight of each sample. The weight of each attribute is proportionate to its importance. Second, using the multivariate relationship analysis approach, the correlation between various factors is determined, and the impact of various factors on college students’ entrepreneurial capacity is further investigated. The results of the experiment demonstrate that adaptability and learning ability are the most important characteristics influencing college students’ entrepreneurial aptitude, whereas self-control has little impact.

Pricing strategies of dual-channel green supply chain considering Big Data information inputs

In the Big Data environment, when green manufacturers invest in the green production technology, to satisfy consumer demand timely and accurately, they may begin to gain consumer performance information (hereafter, CBDI) to design and produce product. However, these will go up their extra costs. Meanwhile, for a green manufacturer who sells the green product through the online channel and the offline channel, the expression of its market demand needs to rethink in the new environment. In these conditions, for a dual-channel green supply chain (hereafter, DGSC), chain members pay more attention on the pricing problems considering the inputs of CBDI and greening R&D. Hence, to resolve this question, a DGSC a green manufacturer selling by the online channel and with one retailer selling by the offline channel was chosen. Afterward, the demand function of the DGSC was revised, and we analyzed the profits models and its pricing rules in the proposed four common cost-sharing models. Results indicate that whether the retailer bears the CBDI costs or the greening R&D costs, the retailer will not change its retail price. If the retailer can bear some CBDI costs, the alteration tendencies of the best wholesale price are related to the cost-sharing parameter.

The Construction of University Network Education System Based on Mobile Edge Computing in the Era of Big Data

This article first established a university network education system model based on physical failure repair behavior at the big data infrastructure layer and then examined in depth the complex common causes of multiple data failures in the big data environment caused by a single physical machine failure, all based on the principle of mobile edge computing. At the application service layer, a performance model based on queuing theory is first established, with the amount of available resources as a conditional parameter. The model examines important events in mobile edge computing, such as queue overflow and timeout failure. The impact of failure repair behavior on the random change of system dynamic energy consumption is thoroughly investigated, and a system energy consumption model is developed as a result. The network education system in colleges and universities includes a user login module, teaching resource management module, student and teacher management module, online teaching management module, student achievement management module, student homework management module, system data management module, and other business functions. Later, the theory of mobile edge computing proposed a set of comprehensive evaluation indicators that characterize the relevance, such as expected performance and expected energy consumption. Based on these evaluation indicators, a new indicator was proposed to quantify the complex constraint relationship. Finally, a functional use case test was conducted, focusing on testing the query function of online education information; a performance test was conducted in the software operating environment, following the development of the test scenario, and the server’s CPU utilization rate was tested while the software was running. The results show that the designed network education platform is relatively stable and can withstand user access pressure. The performance ratio indicator can effectively assist the cloud computing system in selecting a more appropriate option for the migrated traditional service system.

Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

Animation Character Detection Algorithm Based on Clustering and Cascaded SSD

With the evolution of the Internet and information technology, the era of big data is a new digital one. Accordingly, animation IP has been more and more widely welcomed and concerned with the continuous development of the domestic and international animation industry. Hence, animation video analysis will be a good landing application for computers. This paper proposes an algorithm based on clustering and cascaded SSD for object detection of animation characters in the big data environment. In the training process, the improved classification Loss function based on Focal Loss and Truncated Gradient was used to enhance the initial detection effect. In the detection phase, this algorithm designs a small target enhanced detection module cascaded with an SSD network. In this way, the high-level features corresponding to the small target region can be extracted separately to detect small targets, which can effectively enhance the detection effect of small targets. In order to further improve the effect of small target detection, the regional candidate box is reconstructed by a k-means clustering algorithm to improve the detection accuracy of the algorithm. Experimental results demonstrate that this method can effectively detect animation characters, and performance indicators are better than other existing algorithms.

Classifying UNSW-NB15 Network Traffic in the Big Data Framework Using Random Forest in Spark

The focus of this work is on detecting and classifying attacks in network traffic using a binary as well as multi-class machine learning classifier, Random Forest, in a distributed big data environment using Apache Spark. The classifier is tested using the UNSW-NB15 dataset. Major problems in these types of datasets include high dimensionality and imbalanced data. To address the issue of high dimensionality, both information gain as well as principal components analysis (PCA) were applied before training and testing the data using Random Forest in Apache Spark. Binary as well as multi-class Random Forest classifiers were compared in a distributed environment, with and without using PCA, using various number of Spark cores and Random Forest trees, in terms of performance time and statistical measures. The highest accuracy was obtained by the binary classifier at 99.94%, using 8 cores and 30 trees. This study obtained higher accuracy and lower False Alarm Rates than previously achieved, with low testing times.

Digitalization Mining and Protection of Security Language Resources in Big Data Environment

The Baoan language is the language of the Chinese security people. It is also used in some parts of Monguor people. As an important carrier for the dissemination and inheritance of minority culture, it is of great practical significance to excavate and protect the resources of the security language. In the context of modern culture, the minority language, represented by the Baoan dialect, is facing a great survival crisis and communication pressure, and even is on the verge of being lost. Faced with the new big data environment, we should give full play to the technical advantages, use the digital way to excavate and protect the security language resources reasonably and scientifically, and ensure the security language to develop in an orderly way. By analyzing the current situation of security language resources and combining with big data environment, this paper puts forward the strategy of digitalization mining and protection of security language resources.

Construction and Application of Enterprise Internal Audit Data Analysis Model Based on Decision Tree Algorithm

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K‐means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision‐making system based on Fuzzy ID3 and Min‐Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min‐Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk‐oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value.

Construction of Early Warning Mechanism of Network Public Opinion in Colleges and Universities under Big Data Environment

With the advent of the data age, everyone is in the torrent of information. At the same time, with the development and popularization of the Internet, information exchanges between people have become more convenient and faster, and the resulting early warning of network public opinion (NPO) has become an object we have to pay attention to. For colleges and universities, the construction of network public opinion warning (NPOW) is of great significance. This research aims to study the construction of NPO early warning mechanism in colleges and universities under the big data environment. Based on the understanding of the relevant theories of NPO, this article first explains the construction goals and principles of the early warning mechanism of NPO in colleges and universities, and uses a questionnaire survey method to study the necessity and effect of establishing an early warning mechanism of NPO in colleges and universities, and proposes Countermeasures that universities can refer to when constructing an early warning mechanism for NPO. According to the questionnaire "Do you think the school needs to provide early warning of NPO", 10.7% of college students indicated that it is unnecessary. 15.84% of college students think that it doesn't matter to give early warning of NPO, which shows that the current colleges and universities lack in-depth understanding of early warning of NPO.

The Dynamic of Learning Innovation of College Students Based on Big Data Analysis

In the management of modern higher education, the level of information is improving year by year. Over the years, with the wide use of school network system and the accumulation of data in the main business system, the big data environment has gradually formed within the school. This paper will mainly explore the motivation of innovative learning for college students based on this big data environment. This paper mainly analyzes and studies the students' learning behavior data stored in the relevant business systems of colleges and universities, discusses the correlation between students' learning, life and psychology, excavates abnormal data, and makes full use of these analysis results. The purpose of this paper is to use big data related technology to analyze and study students' learning and innovative behavior data in order to make full use of students' behavior data and provide the basis of data analysis for the overall development of students' moral and intellectual ability. This paper analyses the practical ability of college students' innovation from the perspective of colleges and universities in this province, in order to explore the reference significance for the cultivation of college students' innovative practical ability in China. The experimental research shows that 32.3% of the junior students hope to improve their practical ability during college.26.3% hope to master solid professional knowledge. The school should cooperate actively to create an environment for students to improve their learning innovation ability.

Innovation Clustering Analysis of Accurate Support Path for CSE under the Environment of Big Data.

Facing the severe employment situation and social environment, the employment of college students has become a very important issue. The big data analysis plays a positive role in entrepreneurship, which can not only improve the success rate of entrepreneurial path selection, but also accumulate a lot of innovative practical experience for college students. Based on the importance of big data technology for entrepreneurial path, this paper proposes an innovative model of accurate support path for college students' entrepreneurship (CSE), in which the K-means algorithm is applied to the analysis of entrepreneurial support path. Finally, this paper makes an experimental analysis on the model, and the results show that K-means can greatly reduce the computational complexity of the algorithm, and the precision, recall, and F parameter of the model can be effectively improved. The model is of great significance in improving the feasibility of college students' entrepreneurial support policies.

Management Analysis on the Improvement of Educational Service of University Library under the Environment of Big Data

Under the big data (BD) environment, the main work of university library education service is to carry out corresponding business for college students. In this process, we need to constantly improve our professional knowledge, skill level and message literacy. Therefore, in order to achieve this goal, we must pay attention to the effective collection of reader resources. This paper first introduces the concept, management characteristics and functions of university library education, then expounds the principles of improving university library education service, and uses the method of questionnaire to investigate the current situation of university library education service. Finally, the survey results show that students are not satisfied with the overall image of the librarians and the quality of their staff. At the same time, some students believe that at present, colleges and universities have basically established a perfect learning resource system to provide high-quality and efficient message services for the majority of users. Others believe that college students mainly browse when reading, lacking interests and autonomy. In addition, most readers still maintain a wait-and-see attitude towards library education. Most teachers hope to change the working attitude of library staff, improve the management quality and enhance the working ability of staff.

The Application of Database and Big Data Technology in College Ideological and Political Work and Student Work

In the environment of Big Data, database and big data technology are widely used, which provides intellectual impetus and technical support for the innovation of ideological and political work and student work in colleges and universities. In order to innovate the ideological and political system of colleges and universities and optimize the working mode of students, it is necessary to take the advantage of the application of database and big data technology as the basis, and to innovate and reconstruct the ideological and political system of colleges and universities and the working mode of students, we will comprehensively improve the quality of education for our people. Based on the advantages of the application of database and big data technology in ideological and political work and student work in colleges and universities, this paper puts forward some concrete application strategies.

Analysis on the Construction of Marxist Ideological Discourse Power in the Environment of Big Data.

The core of socialist ideology is Marxist discourse power, which refers to Marxism's dominance in ideological education and dissemination as well as its dominant position throughout the entire social ideological system. Big data (BD) has altered how people think, captured recent advancements in the process of thinking development, enriched and advanced thinking science, and improved how people think in relation to the times. This paper examines the BD era's discourse power construction of Marxist ideology. The percentage of respondents who answered "several times a day" when asked if they listen to, watch, or read political news on a daily basis increased from 7.2 percent in 2018 to 17.5 percent in 2021. The percentage of "once a day" and "several times a week" responses also increased, but the response "once or twice a week" did not. Understanding the latest developments and ideas in human thinking in the age of BD can help us think more broadly and creatively, and it can also give us the theoretical groundwork and thinking power to advance the use and development of BD.

Research on Library Personalized Push System Based on Big Data Decision

Big Data is the product of the rapid development and application of information technology, and it is also the inevitable trend of the development and application of intelligent technology. In the environment of big data, by giving full play to the advantages of big data decision-making system and actively constructing library personalized push system, we will break the foundation of Library Service Space and time, fully guarantee the information system and service ability of the library. In the process of operation, libraries should make full use of big data resources and accurately identify the service demand, reading behavior and individual characteristics of library users, innovate the Information Mode and personalized service method, continuously improve the user’s reading experience, improve the user relationship, thus form the accurate, personalized and intelligent book information service system.